How Ford engineers cut costs and prototypes with CAE

The challenges that ever-increasing electrical/electronic complexity
brings to the automotive industry means that we, in the industry,
increasingly rely on computer-aided engineering (CAE) and virtual
design verification tools. Development cycles are getting shorter,
and, to meet customer demand, we have to be able to deliver robust
designs as quickly as possible.

Prior to the availability of advanced EE simulation tools, we
depended on spreadsheet analysis to do simple calculations to help
us study the interaction between components—for example, a control
module and a switch. This approach was limited to studying specific
parameters for components—such as ensuring the switch received
sufficient current to make a good contact. While this form of
analysis was limited, we learned a lot about the importance of data
accuracy and of fully capturing and understanding the data
parameters, so that we could enable accurate results, and create
models that mirrored the real world.

Mirroring the Real World
Creating an analysis that is thorough, accurate and mirrors the real
world is a top priority. Since the tools we use now are far more
sophisticated than our early spreadsheet models, we spend a lot of
resources ensuring that the models we use are accurate, whether they
are sourced in-house or from suppliers.
The alternative to CAE analysis—the use of physical prototypes—is
becoming prohibitively expensive.

In addition to the cost of
building the vehicle itself, program teams must pay to have access
to the vehicle for each day of physical testing. The extensive tests
that we run using CAE analysis tools would take many days of testing
using physical prototypes and can’t even begin to approach the
number of “what-if” scenarios that CAE analysis can cover.

Improving RobustnessAs well as helping to reduce development costs, CAE analysis
makes a significant contribution to the robustness of our designs.
We run hundreds or thousands of analyses with variations of
component tolerances and look at the effects of temperature changes
and aging. We can then use data from the analysis tools to isolate
the component tolerances that matter, and tighten ones of interest,
or make other changes that improve the robustness of the overall
subsystem.

Sometimes, the quality issues we uncover have implications for our
suppliers. A circuit design may pass the verification tests at the
component level, but may need improvement when used in the
subsystem. We work closely with engineers and suppliers, providing
feedback which results in more robust designs.

Developing CAE PlansA typical vehicle CAE plan may have over 500 EE analyses. We use
Monte Carlo analysis extensively to study variations in parameters
across hundreds of scenarios. We also use our CAE environment to
look at DC and transient analysis, as well as sensitivity. Pareto
analysis (Figure 1) helps us with our detective work; we typically
use it to understand what the significant contributors are to signal
variation over many Monte Carlo runs.

Figure 1: Pareto analysis results

An example of one of the types of analysis that we focus on
establishes the robustness of shared signals (Figure 2) among
different modules in the vehicle. One component may be the source of
a signal that is monitored by several other components. The analysis
of shared signals is critical to ensure that designs from several
different component suppliers are able to perform all their
functions with complete accuracy and robustness under all expected
operating conditions. CAE analysis allows us to perform hundreds of
different analyses to get statistically valid results. This is cost
and timing prohibitive when trying to use a physical prototype in
the lab.

Figure 2: Design example using shared signals

Modeling Electro-Mechanical Systems
Electromechanical systems have been and will be increasing their
footprint in vehicle system and subsystem designs.To better
understand the mechanical effects within electro-mechanical systems,
we need to be able to analyze physical components alongside the
electrical ones.For example, we’ll model the mechanical properties
of a motor.

On a more complex level, we have developed an
electro-mechanical model for power window subsystem. The mechanical
teams (who may have little experience with the electrical domain)
can use the model to help them choose what size motor they will
need, or the different torque/power losses in the system they will
use, based on the characteristic interaction of their mechanical and
electrical components.

Prototyping Software
The cost of developing software for automotive applications is
growing enormously, because of the growth of software content in
cars. Software verification is an issue that affects quality and the
performance of safety-critical systems. Now that we have a
methodology in place to reduce our use of physical prototypes for
the electrical and electro-mechanical subsystems, our next area of
focus is the software domain.

If we can have our developers use
virtual models to do functional testing, we can start to reduce our
dependency on the use of breadboards even further, as well as
benefiting from faster system verification, better debug and
improved quality in the software domain. Once we have a virtual
software solution, we will be closer to being able to perform
complete system validation using a combination of virtual
prototyping and CAE analysis.
As we evolve, and the industry evolves, we look forward to virtual
prototyping, with links to Saber, evolving to bring that missing
piece—the software piece—into play.

Note: Ford’s EECAE team supports all vehicle program teams with
electrical computer-aided engineering analysis and design
verification. It also provides vehicle program teams with support
services such as current-based limit testing and with EE design
alternative investigation.About the AuthorsAsaad Makki earned a Ph.D. degree in Electrical and Computer
Engineering from Wayne State University, in 1993. He is currently a
member of the engineering management team at Ford Motor Company,
where he is leading the global electrical CAE group.

Dave Beard began his EE career as a Technician in the U.S.
Navy, working on satellite communication and cryptographic
equipment. After receiving his BSEE from Lawrence Technological
University, he performed robustness circuit analysis on small jet
engine fuel systems with Williams International. Dave moved to Ford,
and has held positions involved with manufacturing quality, value
engineering, systems engineering, design and release, and, for the
past 12 years. Dave’s current position is EECAE Senior Engineer,
based in Dearborn, Mich.